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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 29862

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Special Issue Editor

Dr. Arshian Sharif

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Guest Editor

Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, 06010 Sintok, Malaysia
Interests: energy finance; energy economics; environmental economics

Special Issue Information

Dear Colleagues,

The notion of sustainable development refers to development that meets the needs of the present without compromising the ability of future generations to meet their own needs. There are four interlinked dimensions of sustainable development, i.e., society, environment, culture, and economy. To achieve the goal of sustainable development, it is essential to transform into a low-carbon emissions society. Currently, the most concerned environmental problem is high carbon emissions, which is mainly caused by consuming a large amount of fossil energy. High carbon emissions will bring potential risks to human activities and life. The frequent occurrences of air pollution and extreme weather conditions have seriously threatened human health and property safety. Furthermore, this will continue if carbon emissions are not controlled, and irreversible risks caused by climate change will increase in the future. Thus, to achieve sustainable development as well as a transformation to a low-carbon economy, the governments and policy makers need to pay more attention to renewable energy. This is because renewable energy is an important way to achieve a low-carbon economy. The contributions to this Special Issue are expected to bring new knowledge and insights to this field, which will be of interest to a wide range of stakeholders, including governments, industry, and other policy makers.

Dr. Arshian Sharif
Guest Editor

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Keywords

Keywords: CO2 emission
ecological footprint
economic growth
renewable energy
green technology innovation
sustainable development
corporate social responsibility
energy consumption

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Published Papers (6 papers)

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Research

55 pages, 3649 KiB

Open AccessEditor’s ChoiceArticle

How Efficient Is the Cohesion Policy in Supporting Small and Mid-Sized Enterprises in the Transition to a Low-Carbon Economy?

by Carla Henriques, Clara Viseu, António Trigo, Maria Gouveia and Ana Amaro

Sustainability 2022, 14(9), 5317; https://doi.org/10.3390/su14095317 - 28 Apr 2022

Cited by 8 | Viewed by 2710

Abstract

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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25 pages, 5434 KiB

Open AccessArticle

Case Study on Carbon Footprint Life-Cycle Assessment for Construction Delivery Stage in China

by Xiaojuan Li, Chen Wang, Mukhtar A. Kassem, Shu-Yi Wu and Tai-Bing Wei

Sustainability 2022, 14(9), 5180; https://doi.org/10.3390/su14095180 - 25 Apr 2022

Cited by 15 | Viewed by 6870

Abstract

The construction industry’s high energy consumption and carbon emissions significantly burden the ecological environment. Thus, it is necessary to study measures and strategies for emissions reduction during construction for an improved, safe and sustainable environment. Using the life-cycle assessment method, this study aims to investigate construction-building outcomes and their carbon footprint during the construction delivery stage. This work used a compiled database of carbon-emission factors per unit for concrete and mortar with different densities and 16 building-project case studies in Fujian Province to verify the empirical findings. The results show that general civil engineering works produce more carbon emissions than decoration engineering. Furthermore, cement’s average proportion of carbon emissions relative to total carbon emissions is the largest at 30.26%. Our findings also show a strong linear relationship between the total carbon emissions, eaves height, project cost, and building area during the building construction. The findings in this paper promote the conversion of buildings from high-energy consumption to multi-carbon reduction. The concept of this research contributes to the existing knowledge by proposing a carbon-footprint calculation method and establishing the trend of carbon emissions in building construction. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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21 pages, 2584 KiB

Open AccessArticle

Exploring Patterns of Transportation-Related CO₂ Emissions Using Machine Learning Methods

by Xiaodong Li, Ai Ren and Qi Li

Sustainability 2022, 14(8), 4588; https://doi.org/10.3390/su14084588 - 12 Apr 2022

Cited by 25 | Viewed by 3837

Abstract

While the transportation sector is one of largest economic growth drivers for many countries, the adverse impacts of transportation on air quality are also well-noted, especially in developing countries. Carbon dioxide (CO₂) emissions are one of the direct results of a transportation sector powered by burning fossil-based fuels. Detailed knowledge of CO₂ emissions produced by the transportation sectors in various countries is essential for these countries to revise their future energy investments and policies. In this framework, three machine learning algorithms, ordinary least squares regression (OLS), support vector machine (SVM), and gradient boosting regression (GBR), are used to forecast transportation-based CO₂ emissions. Both socioeconomic factors and transportation factors are also included as features in the study. We study the top 30 CO₂ emissions-producing countries, including the Tier 1 group (the top five countries, accounting for 61% of global CO₂ emissions production) and the Tier 2 group (the next 25 countries, accounting for 35% of total CO₂ emissions production). We evaluate our model using four-fold cross-validation and report four frequently used statistical metrics (

R^{2}

, MAE, rRMSE, and MAPE). Of the three machine learning algorithms, the GBR model with features combining socioeconomic and transportation factors (GBR_ALL) has the best performance, with an

R^{2}

value of 0.9943, rRMSE of 0.1165, and MAPE of 0.1408. We also find that both transportation features and socioeconomic features are important for transportation-based CO₂ emission prediction. Transportation features are more important in modeling for 30 countries, while socioeconomic features (especially GDP and population) are more important when modeling for Tier 1 and Tier 2 countries. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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17 pages, 617 KiB

Open AccessArticle

Impact of Green Finance and Environmental Regulations on the Green Innovation Efficiency in China

by Tong Zhao, Haihua Zhou, Jinde Jiang and Wenyan Yan

Sustainability 2022, 14(6), 3206; https://doi.org/10.3390/su14063206 - 9 Mar 2022

Cited by 62 | Viewed by 7207

Abstract

Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a very important role in regional sustainable development. Data from 31 provinces and cities in China from 2011 to 2020 were used to select the proportion of energy saving and environmental protection costs in GDP as the green financial value, and the proportion of industrial pollution control input in GDP as the environmental regulation index. Green innovation efficiency is measured from two aspects of input and output by DEA method, and carried out for 31 provinces and cities in three regions. Using the DEA-Malmquist index to measure regional green innovation efficiency, the results show that the green innovation efficiency in three regions basically presents an upward trend, but the upward trend of green innovation efficiency is different between the three regions. A Tobit regression model is constructed to explore the impact of green finance and environmental regulations on the green innovation efficiency in these three regions. Research indicates that environmental regulations, the proportion of output value of tertiary industry in GDP, industrial structure, and foreign direct investment have significant impacts on the green innovation efficiency in all regions. Green finance, industrial structure, and power consumption have a significant impact on the green innovation efficiency in eastern China. Industrial structure has a significant impact on green innovation efficiency in central China, while power consumption and industrial structure have a significant impact on green innovation efficiency in western China. Therefore, each region needs to improve the standard of environmental regulation innovation, and introduce and use foreign investment in a scientific and reasonable way so as to promote the improvement of industrial infrastructure. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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20 pages, 4203 KiB

Open AccessArticle

Does Economic Policy Intervention Inhibit the Efficiency of China’s Green Energy Economy?

by Zhiyu Fang, Ling Jiang and Zhong Fang

Sustainability 2021, 13(23), 13412; https://doi.org/10.3390/su132313412 - 3 Dec 2021

Cited by 6 | Viewed by 1929

Abstract

Due to the different focus of policies in different regions, China’s energy efficiency has been unstable in recent years. The changing focus of policies at the same time has also impacted the energy system, and therefore, it is very important to explore the impact of China’s new energy policy on its oil and gas energy efficiency. The practical significance of this research is to integrate three policy intervention factors: incentive economic policy intervention, government financial intervention, and mandatory policy intervention. Through the regression of the Stochastic Frontier Approach model, the influence of these policy intervention factors on the efficiency evaluation of decision-making units is eliminated. We calculate the environmental pollution index as an undesired output to measure the efficiency of policy intervention in the green economy of China’s oil and gas energy, use Luenberger model to explore total factor productivity, and find the main reasons that affect the productivity of the green energy economy. The results show that China’s oil and gas energy construction is currently in the stage of scale economy, but the heavy dependence of China’s energy consumption on foreign imports leads to difficulties and urgency in the present stage of technological progress. After excluding the factors of policy intervention, China’s overall energy is in a slightly insufficient policy environment, and energy efficiency is in an unbalanced state. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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19 pages, 1209 KiB

Open AccessArticle

Pathway towards Sustainability in Selected Asian Countries: Influence of Green Investment, Technology Innovations, and Economic Growth on CO2 Emission

by Rundong Luo, Sami Ullah and Kishwar Ali

Sustainability 2021, 13(22), 12873; https://doi.org/10.3390/su132212873 - 21 Nov 2021

Cited by 72 | Viewed by 4689

Abstract

Green investment and technology innovations are generally considered as an effective factor to mitigate CO2 emissions as these enhance cleaner production and energy efficacy. Thus, this study investigated the influence of green investment, technology innovations, and economic growth on CO2 emissions in selected Asian countries for the period 2001 to 2019. The Cross-Section dependency (CSD) signified the cross-section dependence in the panel countries, whereas CIPS and CADF testing affirmed the stationarity of all variables at the first difference. Consequently, the Westerlund cointegration method recognized a long-term association among variables. The outcomes of Panel Fully Modified OLS and Panel Dynamic OLS results indicated that green investment and technology innovations are helpful in mitigating CO2 emissions in selected Asian countries. In addition, the Environmental Kuznets Curve (EKC) postulate is validated for the given time period and indicated inverted U-shaped linkages between the economic growth and CO2 emission. The outcomes of the remaining variables, including population growth, energy consumption, FDI inflow, and trade, are estimated to have an augmenting influence on CO2 emission. Our results regarding the FDI–CO2 emissions nexus support the presence of the pollution-haven hypothesis. Moreover, the estimated results from PFMOLS and PDOLS are validated by Granger Causality, and AMG and CCEMG tests. The study suggests the adoption of renewable sources as energy input and the promotion of innovations for energy efficiencies to reduce CO2 emissions in Asian economies. Full article

(This article belongs to the Special Issue Low Carbon Economy, Green Innovation, Renewable Energy and Sustainable Development)

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